Final answer:
The term 'sampling error' describes the expected difference between a sample estimate and the true population parameter due to the randomness inherent in sample selection. It can be decreased by increasing the sample size or ensuring a randomized and representative sample selection.
Step-by-step explanation:
The term 'sampling error' accounts for the differences that may occur between the sample and the population from which it was drawn. It is a measure of how much the sample estimate is likely to differ from the actual population parameter due to the randomness of the sample chosen. Sampling error can occur from a variety of sources such as natural variation, measurement error, or because the sample is too small and not adequately representative of the population. To lower sampling error, researchers can increase the sample size or ensure the sample is selected randomly and is representative of the entire population.
For instance, if a group of researchers reported a sampling error of ±3 percent, it means that the true population parameter is expected to be within 3 percentage points above or below the sample estimate. This range gives an indication of the reliability of the sample estimates and reflects the expected variability due to the sample being only a subset of the population.